Domain-specific lexical analysis

ABSTRACT

A method includes performing, at a device, an analysis of a domain-specific corpus to identify a base term and a modifier term. The modifier term modifies the base term in at least a portion of the domain-specific corpus. The method also includes accessing, by the device, a first entry in lexicon data. The first entry includes core data corresponding to domain-independent lexical information for the base term. The method further includes adding, based on the analysis, non-core data to the first entry. The non-core data corresponds to domain-specific lexical information for the base term. The non-core data identifies the modifier term as a domain-specific modifier of the base term.

I. CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application and claims priority fromU.S. patent application Ser. No. 15/679,719, filed on Aug. 17, 2017 andentitled “Domain-Specific Lexical Analysis,” which is incorporated byreference herein in its entirety.

II. BACKGROUND

The present application relates to domain-specific lexical analysis.

III. SUMMARY

In a particular implementation, a method includes performing, at adevice, an analysis on domain-specific corpus to identify a base termand a modifier term. The modifier term modifies the base term in atleast a portion of the domain-specific corpus. The method also includesaccessing, by the device, a first entry in lexicon data. The first entryincludes core data corresponding to domain-independent lexicalinformation for the base term. The method further includes adding, basedon the analysis, non-core data to the first entry. The non-core datacorresponds to domain-specific lexical information for the base term.The non-core data identifies the modifier term as a domain-specificmodifier of the base term.

In another particular implementation, a computer program product fordomain-specific data generation includes a computer-readable storagemedium having program instructions embodied therewith. The programinstructions are executable by a processor to cause the processor toperform operations including performing an analysis on domain-specificcorpus to identify a base term and a modifier term. The modifier termmodifies the base term in at least a portion of the domain-specificcorpus. The operations also include accessing a first entry in lexicondata. The first entry includes core data corresponding todomain-independent lexical information for the base term. The operationsfurther include adding, based on the analysis, non-core data to thefirst entry. The non-core data corresponds to domain-specific lexicalinformation for the base term. The non-core data identifies the modifierterm as a domain-specific modifier of the base term.

In another particular implementation, a system includes a memory and alexical analyzer. The memory is configured to store lexicon data. Thelexical analyzer is configured to perform an analysis on domain-specificcorpus to identify a base term and a modifier term. The modifier termmodifies the base term in at least a portion of the domain-specificcorpus. The lexical analyzer is also configured to access a first entryin the lexicon data, the first entry including core data correspondingto domain-independent lexical information for the base term. The lexicalanalyzer is further configured to add, based on the analysis, non-coredata to the first entry. The non-core data corresponds todomain-specific lexical information for the base term. The non-core dataidentifies the modifier term as a domain-specific modifier of the baseterm.

IV. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a cloud computing environment according to an aspectof the disclosure.

FIG. 2 illustrates abstraction model layers according to an aspect ofthe disclosure.

FIG. 3 illustrates a system for domain-specific lexical analysis.

FIG. 4 illustrates a system for lexically-guided parsing.

FIG. 5 illustrates example parse trees generated by the system of FIG.3.

FIG. 6 illustrates a set of examples of lexicon data entries generatedby the system of FIG. 3.

FIG. 7 illustrates examples of domain-specific parsing rules generatedby the system of FIG. 3.

FIG. 8 illustrates examples of input text processed by the system ofFIG. 4.

FIG. 9 illustrates a flowchart of a method of domain-specific lexicalanalysis.

FIG. 10 illustrates a flowchart of a method of lexically-guided parsing.

FIG. 11 illustrates a block diagram of a computing environment accordingto an aspect that includes electronic components through which thedescribed systems may be implemented.

V. DETAILED DESCRIPTION

Systems and methods of domain-specific lexical analysis anddomain-specific pre-parsing are disclosed. Natural language processinguses lexical data to parse language samples (e.g., a text). In manylanguages, a particular word can have different meanings depending oncontext. For example, when a particular word is used in text of aparticular technical field (e.g., in a domain-specific context), theword may have a different meaning or nuance than when the word ispresent in general usage (e.g., in a domain-independent context).Manually adapting a general purpose (e.g., domain-independent)rule-based parser to a specialized domain (e.g., medicine) isnon-trivial (e.g., complicated, time-consuming, and very likelyincomplete). Specialized domains may introduce syntactic patterns andmay present with syntactic ambiguity types that are less common in thegeneral domain. Automating (or semi-automating) rule creation, asdescribed herein, conserves resources (e.g., time) and may result in amore robust parser (e.g., fewer errors and greater coverage).

According to techniques described herein, during a training phase, alexical analyzer (e.g., a processor) may generate domain-specificparsing rules based on analyzing a domain-specific corpus associatedwith a domain (e.g., medicine). The lexical analyzer may also update adatabase of lexicon data based on analyzing the domain-specific corpus.The lexicon data may be previously generated, received from anotherdevice, or both. The lexicon data may include domain-independentinformation (e.g., core data), such as parts of speech of base terms(e.g., nouns). The lexical analyzer may update the lexicon data toinclude domain-specific information (e.g., non-core data) correspondingto the base terms. For example, the lexical analyzer may analyze largebodies of domain-specific texts to generate co-occurrence statistics ofhead-modifier pairs in the domain-specific texts. The lexical analyzermay determine, based on the co-occurrence statistics, that particularterms (e.g., “high”, “blood”, or “plasma”) appear to modify a base term(e.g., “cholesterol”) in at least a portion of a domain-specific corpus,as described herein. The lexical analyzer may update the lexicon data toindicate that the particular terms are usable as modifier terms of thebase term in the domain (e.g., medicine).

The lexical analyzer may generate domain-specific parsing rulescorresponding to the modifier terms and the base terms, as describedherein. For example, the lexical analyzer may generate a collocationrule (e.g., left attachment of adjectival modifier terms) in response todetermining that modifier terms (e.g., “high”, “bad”, “elevated”, and“good”) of a particular modifier type (e.g., adjectival modifier terms)are detected in a particular position relative to (e.g., prior to)corresponding base terms in the domain-specific corpus. The trainingphase may happen offline (e.g., prior to running in production mode).The domain-specific parsing rules and the domain-specific information ofthe lexicon data may be used to train a domain-specific parser. Forexample, the lexical analyzer may provide the domain-specific parsingrules and the lexicon data (e.g., including the domain-specificinformation) to the domain-specific parser.

During a runtime phase, a parser that includes the domain-specificparser and a domain-independent parser (e.g., a general purpose parser)may parse input text. The input text parsed during the runtime phase maydiffer from the domain-specific corpus analyzed during the trainingphase. The lexical analyzer analyzes, during the training phase, thedomain-specific corpus to generate the domain-specific parsing rules andthe domain-specific information of the lexicon data. During the runtimephase, the parser uses the domain-specific parsing rules and the lexicondata, in addition to domain-independent parsing rules, to parse theinput text. For example, the domain-specific parser may analyze inputtext based on the lexicon data and the domain-specific parsing rules, asdescribed herein. The domain-specific parser may generate partiallyparsed and bracketed text by analyzing the input text (e.g., “Thepatient suffers from high blood cholesterol.”), as described herein. Thepartially parsed and bracketed text (e.g., “The patient suffers from[high [blood cholesterol]]”) may indicate phrasal boundary attachmentsthat are valid in the domain (e.g., medicine). The domain-specificparser may provide the partially parsed and bracketed text to adomain-independent parser (e.g., a general purpose parser).

The domain-independent parser may, in response to receiving thepartially parsed and bracketed text from the domain-specific parser,generate parsed text by analyzing the partially parsed and bracketedtext based on domain-independent parsing rules. The domain-independentparsing rules may be previously generated, received from another device,or both. In a particular example, the partially parsed and bracketedtext may correspond to an intermediate parse tree. Thedomain-independent parser may generate a parse tree corresponding to theparsed text by analyzing the intermediate parse tree based on thedomain-independent parsing rules.

The lexical analyzer, the domain-specific parser, and thedomain-independent parser may be useful in various applications. Forexample, the lexical analyzer may generate domain-specific parsing rulesand update lexicon data based on analyzing a domain-specific corpus(e.g., research papers) associated with a domain (e.g., medicine). Anemergency medical technician (EMT) may upload patient notes to ahospital system upon examination of a patient in an ambulance. Thepatient notes (e.g., input text) may be analyzed by the domain-specificparser and the domain-independent parser to generate parsed text. Thehospital system may generate an alert in response to determining thatthe parsed text indicates that particular conditions have been detected.The alert may enable the appropriate resources (e.g., equipment, medicalstaff, medicines, or a combination thereof) to be prepared to treat thepatient when the ambulance arrives at a hospital.

As another example, during a breakout of a rare disease, a doctor maylocate a large number of research papers associated with the raredisease. The lexical analyzer may generate domain-specific parsing rulesand update lexicon data based on a few of the research papers. Thedomain-specific parser and the domain-independent parser may analyze theresearch papers (e.g., all of the research papers) to generate parsedtext. The parsed text may be used to populate a research database.Searching the research database for relevant information may conservetime, as compared to reading each of the research papers.

It should be understood that medicine is used as an illustrativeexample, and the domain may correspond to any specialized domain, suchas an area of study (e.g., engineering, law, medicine, or chemistry), alanguage (e.g., French, Spanish, or Italian), a programming language(e.g., Java® (registered trademark of Oracle, Inc., Redwood Shores,Calif.), Python® (registered trademark of Python Software Foundation,Delaware), etc.), another specialized domain, or a combination thereof.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, aspectsof the present disclosure are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. In some implementations,this cloud model may include at least five characteristics, at leastthree service models, and at least four deployment models, as describedherein.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e. g., mobile phones, laptops, andPDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e. g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N, may communicate.

Nodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 50 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. One or more of the nodes 10 may include a lexical analyzer 108,a domain-specific lexically-driven pre-parser 110, or both. The lexicalanalyzer 108, the domain-specific lexically-driven pre-parser 110, orboth, may correspond to infrastructure, platforms, and/or softwareprovided as services by the cloud computing environment 50. The lexicalanalyzer 108 may be configured to analyze a domain-specific corpus togenerate domain-specific information (e.g., non-core data),domain-specific parsing rules, or a combination thereof, as furtherdescribed with reference to FIG. 3. The domain-specific corpus may beassociated with a domain. The lexical analyzer 108 may update lexicondata to indicate the non-core data associated with the domain, asfurther described with reference to FIG. 3.

The domain-specific lexically-driven pre-parser 110 may be configured togenerate partially parsed and bracketed input text by analyzing inputtext based on the updated lexicon data and the domain-specific parsingrules, as further described with reference to FIGS. 4-5. Adomain-independent rule-based parser may generate parsed text byanalyzing the partially parsed and bracketed input text based ondomain-independent parsing rules, as further described with reference toFIGS. 4-5. Applying the domain-independent parsing rules to thepartially parsed and bracketed input text may result in fewer parsingerrors (e.g., no errors), as compared to applying the domain-independentparsing rules directly to the input text. For example, the partiallyparsed and bracketed input text may indicate phrasal boundaries that arevalid in the domain and may thus resolve at least some syntacticambiguity that would otherwise have resulted in parsing errors.

It is understood that the types of computing devices 54A-N shown in FIG.1 are intended to be illustrative only and that computing nodes 10 andcloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring to FIG. 2, a set of functional abstraction layers provided bycloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and aspects of thedisclosure are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some aspects, software components includenetwork application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and domain-specific analysis 96. In aparticular aspect, the domain-specific analysis 96 may includedomain-specific lexical analysis, as described herein with reference tothe lexical analyzer 108. In a particular aspect, the domain-specificanalysis 96 may include domain-specific lexically-driven pre-parsing, asdescribed herein with reference to the domain-specific lexically-drivenpre-parser 110.

FIG. 3 illustrates a system 300 for performing domain-specific lexicalanalysis. The system 300 includes a device 302. The device 302 mayinclude a processor, a computer, a laptop computer, a server, acommunication device, an entertainment device, or a combination thereof.The device 302 includes (or accesses) the lexical analyzer 108, a textparser 304, a memory 306, or a combination thereof. The lexical analyzer108 may correspond to software, such as instructions executable by aprocessor to perform one or more operations described with reference toFIGS. 1-11. In a particular aspect, the lexical analyzer 108 maycorrespond to a processor configured to perform one or more operationsdescribed with reference to FIGS. 1-11. The text parser 304 includes thedomain-specific lexically-driven pre-parser 110. The domain-specificlexically-driven pre-parser 110 may correspond to software, such asinstructions executable by a processor to perform one or more operationsdescribed with reference to FIGS. 1-11. In a particular aspect, thedomain-specific lexically-driven pre-parser 110 may correspond to aprocessor configured to perform one or more operations described withreference to FIGS. 1-11.

In a particular aspect, the device 302 may correspond to one or more ofthe cloud computing nodes 10 of FIG. 1. For example, the device 302 mayprovide the lexical analyzer 108 (e.g., software corresponding to thelexical analyzer 108) or functions of the lexical analyzer 108 as aservice. In an alternate aspect, the device 302 may correspond to acloud consumer device, such as, for example, the personal digitalassistant (PDA) or cellular telephone 54A, the desktop computer 54B, thelaptop computer 54C, the automobile computer system 54N of FIG. 1, or acombination thereof. The device 302 may receive the lexical analyzer 108(e.g., software corresponding to the lexical analyzer 108) or accessfunctions of the lexical analyzer 108 as a service provided by one ormore of the cloud computing nodes 10 of FIG. 1.

The memory 306 may be configured to store lexicon data 316. The lexicondata 316 may be previously generated by the device 302, received by thedevice 302 from another device, provided by a user 301 to the device302, or a combination thereof. The lexicon data 316 may correspond to adata structure (e.g., a table) arranged to have one or more entries.Each entry of the lexicon data 316 may include a base term (e.g., anoun), core data associated with the base term, or both. The core datamay indicate domain-independent information associated with the baseterm. The domain-independent information may indicate a part of speech(e.g., noun) of the base term, one or more semantic types (or semanticcategories) of the base term, or a combination thereof.

A semantic type may include physical object, conceptual entity,activity, phenomenon, process, or another semantic type. A semantic typemay correspond to one or more additional semantic types (e.g.,sub-types) that correspond to a higher level of detail or a narrowerclassification. For example, physical object may include an organism, ananatomical structure, a manufactured object, a substance, or anothertype of physical object.

In a particular aspect, various entries of the lexicon data 316 mayindicate semantic types at distinct levels of detail. For example, anentry of the lexicon data 316 may indicate a first semantic type (e.g.,plant) of a corresponding base term (e.g., “aloe”), and another entry ofthe lexicon data 316 may indicate a second semantic type (e.g.,substance) of a corresponding base term (e.g., “cholesterol”). The firstsemantic type (e.g., plant) may correspond to a higher level of detailthan the second semantic type (e.g., substance). For example, the firstsemantic type (e.g., plant) may correspond to a semantic sub-sub-type(e.g., physical object→organism→plant) and the second semantic type(e.g., substance) may correspond to a semantic sub-type (e.g., physicalobject→substance).

In a particular example, the lexicon data 316 includes an entry 318. Theentry 318 includes a base term 322 (e.g., “cholesterol”). The entry 318may include core data 330 associated with the base term 322. The coredata 330 may indicate a part of speech (e.g., noun) of the base term 322(e.g., “cholesterol”). The core data 330 may indicate a first semantictype (e.g., substance), a second semantic type (e.g., condition), one ormore additional semantic types of the base term 322, or a combinationthereof.

The lexical analyzer 108 is configured to generate non-core data basedon analyzing a domain-specific corpus 314, user input 382, or acombination thereof, as described herein. For example, the lexicalanalyzer 108 may generate non-core data 340 associated with the baseterm 322 (e.g., “cholesterol”), as described herein. The domain-specificcorpus 314 is associated with a domain 320 (e.g., medicine). The domain320 may correspond to an area of study (e.g., medicine, engineering,art, music, finance, oil & gas, etc.), a language (e.g., English,French, Spanish, etc.), a programming language (e.g., Java® (registeredtrademark of Oracle, Inc., Redwood Shores, Calif.), Python® (registeredtrademark of Python Software Foundation, Delaware), etc.), anotherdomain, or a combination thereof.

The non-core data may indicate domain-specific information associatedwith base terms. For example, the non-core data 340 may indicatedomain-specific information associated with the base term 322 (e.g.,“cholesterol”). To illustrate, the non-core data 340 may indicate one ormore modifier terms that are usable to modify the base term 322 in thedomain 320. A modifier term may include at least one of an adjectivalmodifier term, a preposition modifier term, a nominal modifier term, oranother modifier term. An adjectival modifier term may correspond to anadjective as a modifier term of a base term. A nominal modifier term maycorrespond to a noun as a modifier term of a base term. A nominalmodifier term (e.g., “blood”) may function as an adjective in relationto the base term (e.g., “cholesterol”) in a phrase (e.g., “bloodcholesterol”). A preposition modifier term may correspond to apreposition as a modifier term of a base term.

A nominal modifier term may include a nominal pre-modifier term or anominal post-modifier term. An adjectival modifier term may include anadjectival pre-modifier term or an adjectival post-modifier term. Apreposition modifier term may include a preposition post-modifier term.A pre-modifier term (e.g., a nominal pre-modifier term or an adjectivalpre-modifier term) may be prior to the base term in a phrase. Forexample, a pre-modifier term (of the domain 320) may be to the left of acorresponding base term in a phrase if phrases in the domain 320 are tobe read from left to right. A post-modifier term may be subsequent tothe base term in a phrase. For example, a post-modifier term (of thedomain 320) may be to the right of a corresponding base term if phrasesin the domain 320 are to be read from left to right.

The lexical analyzer 108 is configured to generate domain-specificparsing rules 370 based on analyzing the domain-specific corpus 314, theuser input 382, or a combination thereof, as further described withreference to FIG. 7. The domain-specific parsing rules 370 may includeat least one of a collocation rule, a morpho-semantic rule, anamed-entity-based pattern rule, or a semantico-syntactic pattern rule,as described with reference to FIG. 7. A collocation rule may indicatewhether a modifier term of a particular type is a pre-modifier term or apost-modifier term. For example, a first collocation rule may indicatethat an adjectival modifier term is a pre-modifier term, and a secondcollocation rule may indicate that a preposition modifier term is apost-modifier term.

A morpho-semantic rule may indicate whether a particular term is usable(e.g., valid) as a modifier term of a term having particular semanticfeatures. For example, a particular morpho-semantic rule may indicatethat a term having particular semantic features (e.g., low, high, orelevated) is not valid as modifier term of a term having a particularprefix (e.g., “hyper”). The particular semantic features may correspondto an “intensity” semantic feature.

A named-entity-based pattern rule may indicate a pattern of terms, wherethe pattern includes one or more named entities. A named entitygenerally includes a word (or a group of words) that identifies anentity by name and which belongs to a particular semantic type. Forexample, the particular semantic type may include person, event, date,organization, place, artifact, or monetary expression. In anotherexample, the particular semantic type may be more fine-grained, such asperson_name, person_role, or event_sporting. In a particular aspect, theparticular semantic type may be even more specific, such asperson_name_author, or event_sporting_football. Variousnamed-entity-based pattern rules may be formed corresponding tonamed-entities X, Y and Z, such as X will take place on Z at Y, or Y isthe location for the X of Z, where X has a semantic type of event, Y hasa semantic type of place, and Z has a semantic type of date.

A semantico-syntactic pattern rule may indicate a pattern of terms,where the pattern indicates phrase types and semantic types of one ormore terms. For example, a particular semantico-syntactic pattern rule(e.g., [action] [prep] {substance | drug}) may indicate that an actionphrase (e.g., “prescribing”) followed by a preposition (e.g., “of”)followed by a term (e.g., “acetaminophen”) having a first semantic type(e.g., substance) or a second semantic type (e.g., drug) satisfies theparticular semantico-syntactic pattern rule.

The lexical analyzer 108 may provide the domain-specific parsing rules370 to the domain-specific lexically-driven pre-parser 110. Thedomain-specific lexically-driven pre-parser 110 is configured togenerate partially parsed and bracketed text based on thedomain-specific parsing rules 370, as further described with referenceto FIG. 4. For example, the domain-specific lexically-driven pre-parser110 may generate partially parsed and bracketed text by applying thedomain-specific parsing rules 370 to input text, as described herein.The partially parsed and bracketed text may correspond to (e.g.,represent) an intermediate parse tree, as further described withreference to FIG. 5. The partially parsed and bracketed text (e.g., theintermediate parse tree) may indicate phrasal boundary attachments thatare valid in the domain 320. A domain-independent rule-based parser maybe configured to generate parsed text based on the output of thedomain-specific lexically-driven pre-parser 110, as further describedwith reference to FIG. 4. For example, the domain-independent rule-basedparser may generate a parse tree by applying domain-independent parsingrules to the intermediate parse tree, as further described withreference to FIG. 5. The parse tree may correspond to (e.g., represent)the parsed text. It should be understood that a parse tree is used as anillustrative example, the parsed text (or the partially parsed andbracketed text) may be represented in various ways.

During operation, the lexical analyzer 108 may determine that thedomain-specific corpus 314 is to be analyzed. For example, the lexicalanalyzer 108 may receive the user input 382 from the user 301 indicatingthat the domain-specific corpus 314 is to be analyzed. The lexicalanalyzer 108 may be configured to analyze the domain-specific corpus 314as corresponding to the domain 320. In a particular aspect, the lexicalanalyzer 108 determines whether the domain-specific corpus 314 isassociated with the domain 320. For example, the lexical analyzer 108may use a heuristic-based approach to determine that the domain-specificcorpus 314 is likely to be associated with the domain 320. As anotherexample, the lexical analyzer 108 may receive the user input 382 fromthe user 301 (or data from another device) indicating that thedomain-specific corpus 314 is associated with the domain 320. Forexample, the user input 382 (or the data) may include an identifier ofthe domain-specific corpus 314 (e.g., a file identifier) and anidentifier (e.g., “#medicine”) of the domain 320.

The lexical analyzer 108 may generate terms (e.g., words) by parsing thedomain-specific corpus 314. The lexical analyzer 108 may compare theterms to base terms indicated by the lexicon data 316. The lexicalanalyzer 108 may generate co-occurrence statistics 380 corresponding tobase terms indicated by the lexicon data 316. For example, the lexicalanalyzer 108 may, in response to determining that the lexicon data 316includes the base term 322 (e.g., “cholesterol”), generate theco-occurrence statistics 380 to indicate a number of times another termappears to modify the base term 322 in at least a portion of thedomain-specific corpus 314. The base term 322 and the other term maycorrespond to a head-modifier pair. The lexical analyzer 108 maydetermine that another term appears to modify the base term 322 inresponse to detecting the other term in proximity (e.g., next) to thebase term 322 in the domain-specific corpus 314. For example, theco-occurrence statistics 380 may indicate that a first term (e.g.,“high”) has occurred a first number of times next to and before the baseterm 322 (e.g., “cholesterol”), a second term (e.g., “blood”) hasoccurred a second number of times next to and before the base term 322,and a third term (e.g., “in”) has occurred a third number of times nextto and after the base term 322.

The lexical analyzer 108 may designate an identified term as a modifierterm of the base term 322 in response to determining that theco-occurrence statistics 380 indicate that the identified term appearsto modify the base term 322 at least a threshold number of times in thedomain-specific corpus 314. For example, the lexical analyzer 108 may,in response to determining that the first number of times satisfies thethreshold, designate the first term (e.g., “high”) as a modifier term334 of the base term 322. The lexical analyzer 108 may, in response todetermining that the second number of times satisfies the threshold,designate the second term (e.g., “blood”) as a modifier term 324 of thebase term 322. The lexical analyzer 108 may, in response to determiningthat the third number of times satisfies the threshold, designate thethird term (e.g., “in”), as a modifier term 344 of the base term 322.

In a particular aspect, the lexical analyzer 108 may determine that aparticular term (e.g., “expensive”) appears to modify the base term 322in the domain-specific corpus 314 (e.g., “expensive cholesterolmedicine”). The lexical analyzer 108 may determine that theco-occurrence statistics 380 indicate that the particular term (e.g.,“expensive”) appears to modify the base term 322 (e.g., “cholesterol”) aparticular number of times. The lexical analyzer 108 may, in response todetermining that the particular number of times fails to satisfy thethreshold (e.g., 20), refrain from designating the particular term(e.g., “expensive”) as a modifier term of the base term 322. In aparticular aspect, the lexical analyzer 108 may determine that anotherbase term (e.g., medicine) is subsequent to the base term 322 in thedomain-specific corpus 314 (e.g., “expensive cholesterol medicine”) andthat the particular term (e.g., “expensive”) appears to modify the otherbase term (e.g., medicine) a second number of times. The lexicalanalyzer 108 may, in response to determining that the particular numberof times is less than the second number of times, refrain fromdesignating the particular term (e.g., expensive) as a modifier term ofthe base term 322.

The lexical analyzer 108 may identify, based on the lexicon data 316, apart of speech of a modifier term, as described herein. The lexicon data316 may indicate, for the modifier term, one or more adjectives 331, oneor more prepositions 333, or a combination thereof. The lexical analyzer108 may determine that the modifier term 334 (e.g., “high”) correspondsto an adjective in response to determining that the adjectives 331include the modifier term 334 (e.g., “high”). The lexical analyzer 108may determine that the modifier term 344 (e.g., “in”) corresponds to apreposition in response to determining that the prepositions 333 includethe modifier term 344 (e.g., “in”). The lexical analyzer 108 maydetermine that the modifier term 324 (e.g., “blood”) corresponds to anoun in response to determining that the modifier term 324 (e.g.,“blood”) is indicated as a particular base term in the lexicon data 316and that the lexicon data 316 indicates that the part of speech of theparticular base term is a noun.

The lexical analyzer 108 may, in response to determining that themodifier term 334 (e.g., “high”) corresponds to a particular part ofspeech (e.g., adjective), determine that modifier term 334 (e.g.,“high”) corresponds to a first modifier type (e.g., an adjectivalmodifier term). The lexical analyzer 108 may, in response to determiningthat the modifier term 334 (e.g., “high”) occurred next to and prior tothe base term 322, determine that the modifier term 334 corresponds to asecond modifier type (e.g., a pre-modifier term). The lexical analyzer108 may generate (or update) the non-core data 340 to indicate that themodifier term 334 (e.g., “high”) is a domain-specific modifier of thebase term 322 of a type that indicates the first modifier type (e.g.,adjectival modifier term), the second modifier type (e.g., pre-modifierterm), or both (e.g., adjectival pre-modifier term). A particulardomain-specific modifier of the base term 322 may be usable to modifythe base term 322 in text associated with the domain 320.

The lexical analyzer 108 may generate the domain-specific information(e.g., the non-core data 340) of the lexicon data 316 during an offlinetraining phase. For example, the lexical analyzer 108 may provide thedomain-specific information (e.g., the non-core data 340) of the lexicondata 316 to the text parser 304 to train the domain-specificlexically-driven pre-parser 110. During the offline training phase, thelexical analyzer 108 may also generate the domain-specific parsing rules370, as described herein. During a runtime phase, the domain-specificlexically-driven pre-parser 110 may process input text based on thedomain-specific information of the lexicon data 316, the domain-specificparsing rules 370, or a combination thereof, to generate partiallyparsed and bracketed input text, as further described with reference toFIG. 4. A domain-independent rule-based parser may generate parsed textbased on the partially parsed and bracketed input text, as furtherdescribed with reference to FIG. 4.

In a particular aspect, the lexical analyzer 108 may generate a firstcollocation rule (e.g., left attachment of adjectival phrases) based atleast in part on determining that the modifier term 334 occurred next toand prior to the base term 322 in the domain-specific corpus 314, asfurther described with respect to FIG. 7. The domain-specific parsingrules 370 may include the first collocation rule. The domain-specificparsing rules 370 are associated with the domain 320.

The lexical analyzer 108 may, in response to determining that themodifier term 344 (e.g., “in”) corresponds to a particular part ofspeech (e.g., preposition), generate (or update) the non-core data 340to indicate that the modifier term 344 (e.g., “in”) is a domain-specificmodifier of the base term 322 corresponding to the particular part ofspeech (e.g., a preposition modifier term). The lexical analyzer 108 maygenerate a second collocation rule (e.g., right attachment ofprepositional phrases) based at least in part on determining that themodifier term 344 occurred next to and subsequent to the base term 322in the domain-specific corpus 314, as further described with respect toFIG. 7. The domain-specific parsing rules 370 may include the secondcollocation rule.

The lexical analyzer 108 may, in response to determining that themodifier term 324 (e.g., “blood”) corresponds to a particular part ofspeech (e.g., noun), determine that the modifier term 324 corresponds toa first modifier type (e.g., a nominal modifier term). The lexicalanalyzer 108 may, in response to determining that the modifier term 324(e.g., “blood”) occurred next to and prior to the base term 322,determine that the modifier term 324 corresponds to a second modifiertype (e.g., a pre-modifier term). The lexical analyzer 108 may generate(or update) the non-core data 340 to indicate that the modifier term 324(e.g., “blood”) is a domain-specific modifier of the base term 322 of atype that indicates the first modifier type (e.g., a nominal modifierterm), the second modifier type (e.g., a pre-modifier term), or both(e.g., a nominal pre-modifier term). The lexical analyzer 108 maygenerate a third collocation rule (e.g., left attachment of nominalpre-modifier terms) based at least in part on determining that themodifier term 324 occurred next to and prior to the base term 322 in thedomain-specific corpus 314, as further described with respect to FIG. 7.The domain-specific parsing rules 370 may include the third collocationrule.

In a particular aspect, a modifier type (e.g., a pre-modifier term or apost-modifier term) of a modifier term may indicate a collocation rule.For example, a modifier term (e.g., “high”) of a first modifier type(e.g., a pre-modifier term) may indicate a first collocation rule (e.g.,left attachment). The lexical analyzer 108 may generate (or update) thedomain-specific parsing rules 370 to include one or more rules based onthe domain-specific corpus 314, as further described with reference toFIG. 7.

In a particular aspect, the lexical analyzer 108 may display proposedupdates to a display of the device 302. The proposed updates mayindicate updates to the lexicon data 316, the domain-specific parsingrules 370, or a combination thereof. The user 301 may provide the userinput 382 to the device 302 indicating edits to the proposed updates,approval of the proposed updates, or rejection of the proposed updates.The lexical analyzer 108 may, in response to determining that the userinput 382 indicates edits or approval of the proposed updates, updatethe lexicon data 316, the domain-specific parsing rules 370, or acombination thereof. The lexical analyzer 108 may thus enable the user301 to monitor updates to the lexicon data 316, the domain-specificparsing rules 370, or a combination thereof. Alternatively, the lexicalanalyzer 108 may, in response to determining that the user input 382indicates that the proposed updates are rejected, refrain from updatingthe lexicon data 316 and refrain from updating the domain-specificparsing rules 370.

In a particular aspect, the lexical analyzer 108 may generate (orupdate) the lexicon data 316 based on the user input 382. For example,the user 301 may provide the user input 382 to the device 302. The userinput 382 may indicate that a term (e.g., “extremity”) is to be added tothe lexicon data 316 as a base term. The user input 382 may indicatedomain-independent information associated with the term (e.g.,“extremity”). For example, the user input 382 may indicate a part ofspeech (e.g., noun), one or more semantic types (e.g., bodypart, point,limit, and state), or a combination thereof, of the term (e.g.,“extremity”). The lexical analyzer 108 may, in response to receiving theuser input 382, generate (or update) the lexicon data 316 to include anentry indicating the term (e.g., “extremity”) as a base term.

In a particular aspect, the lexical analyzer 108 may generate (orupdate) the non-core data 340 based on the user input 382. For example,the user 301 may provide the user input 382 to the device 302. The userinput 382 may indicate that a term (e.g., “elevated”) is to be added tothe non-core data 340 as a modifier of the base term 322 (e.g.,“cholesterol”). The user input 382 may indicate a part of speech of themodifier (e.g., adjective). The lexical analyzer 108 may, in response toreceiving the user input 382, generate (or update) the non-core data 340to indicate that a modifier term (e.g., “elevated”) is a domain-specificmodifier of the base term 322 corresponding to the part of speech (e.g.,an adjectival modifier term). The lexical analyzer 108 may thus enablethe user 301 to manually add a modifier term to the non-core data 340independently of the domain-specific corpus 314.

In a particular aspect, the non-core data 340 is based on thedomain-specific corpus 314 and the user input 382. For example, thenon-core data 340 may include a term (e.g., “elevated”) based on theuser input 382, and may include the modifier term 324, the modifier term334, and the modifier term 344 based on the domain-specific corpus 314.

In a particular aspect, the lexical analyzer 108 may generate (orupdate) the domain-specific parsing rules 370 based on the user input382. For example, the user 301 may provide the user input 382 to thedevice 302. The user input 382 may indicate that a rule (e.g., acollocation rule, a morpho-semantic rule, a named-entity-based patternrule, a semantico-syntactic pattern rule, or another rule) is to beadded to the domain-specific parsing rules 370, as further describedwith reference to FIG. 7. The lexical analyzer 108 may, in response toreceiving the user input 382, generate (or update) the domain-specificparsing rules 370 to include the user-specified rule. The lexicalanalyzer 108 may thus enable the user 301 to manually add a rule to thedomain-specific parsing rules 370 independently of the domain-specificcorpus 314.

In a particular aspect, the domain-specific parsing rules 370 are basedon the domain-specific corpus 314 and the user input 382. For example,the domain-specific parsing rules 370 may include a semantico-syntacticpattern rule based on the user input 382 and may include a firstcollocation rule (e.g., right attachment of prepositional phrases) and asecond collocation rule (e.g., left attachment of adjective phrases)based on the domain-specific corpus 314, as further described withreference to FIG. 7.

The non-core data 340 is associated with the domain 320. For example,the non-core data 340 may indicate the domain 320. In a particularaspect, the entry 318 may include additional non-core data associatedwith one or more additional domains that are distinct from the domain320. For example, the domain 320 corresponds to one of medicine,engineering, art, music, finance, oil & gas, English, French, Spanish,Java® (registered trademark of Oracle, Inc., Redwood Shores, Calif.),Python® (registered trademark of Python Software Foundation, Delaware),or a combination thereof, and a second domain corresponds to another ofmedicine, engineering, art, music, finance, oil & gas, English, French,Spanish, Java® (registered trademark of Oracle, Inc., Redwood Shores,Calif.), Python® (registered trademark of Python Software Foundation,Delaware), or a combination thereof. The lexical analyzer 108 maygenerate the additional non-core data based on analyzing a seconddomain-specific corpus associated with the second domain. The additionalnon-core data may indicate one or more modifier terms as one or moreadditional domain-specific modifiers of the base term 322 (e.g.,“cholesterol”) that are valid in the second domain.

In a particular aspect, the lexical analyzer 108 may, based on the userinput 382, the co-occurrence statistics 380, or both, identify amodifier term as a preferred domain-specific modifier of a base term inthe lexicon data 316. For example, the user 301 may provide the userinput 382 indicating that the modifier term 324 (e.g., “blood”) is apreferred domain-specific modifier term of the base term 322 (e.g.,“cholesterol”). The lexical analyzer 108 may, in response to receivingthe user input 382, determine that the modifier term 324 is a preferreddomain-specific modifier term of the base term 322. In another aspect,the lexical analyzer 108 may, in response to determining that the firstnumber of times that the modifier term 324 (e.g., “blood”) appears tomodify the base term 322 (e.g., “cholesterol”) satisfies (e.g., isgreater than) a preference threshold, determine that the modifier term324 is a preferred domain-specific modifier term of the base term 322.The lexical analyzer 108 may, in response to determining that themodifier term 324 is a preferred domain-specific modifier term, generate(or update) the non-core data 340 to indicate that the modifier term 324is a preferred domain-specific modifier term.

The lexical analyzer 108 may provide the domain-specific parsing rules370 to the domain-specific lexically-driven pre-parser 110. The textparser 304 may parse text based on the domain-specific parsing rules370, the lexicon data 316, or a combination thereof, as furtherdescribed with reference to FIG. 4. In a particular aspect, the device302 may provide the domain-specific parsing rules 370, the lexicon data316, or a combination thereof, to one or more other devices. Forexample, the device 302 may provide the domain-specific parsing rules370, the lexicon data 316, or a combination thereof, to one of the cloudcomputing nodes 10 or one of the computing devices 54A-N. The otherdevice may include a parser (e.g., the text parser 304) configured toparse input text based on the domain-specific parsing rules 370, thelexicon data 316, or a combination thereof.

The system 300 enables generation (or update) of the lexicon data 316(e.g., the non-core data 340), the domain-specific parsing rules 370, ora combination thereof, based on the user input 182, the domain-specificcorpus 314, or both. For example, large texts may be analyzedautomatically (or partially automatically) by the lexical analyzer 108to efficiently update (e.g., train) the lexicon data 316, thedomain-specific parsing rules 370, or a combination thereof. Automatic(or at least partially automatic) generation (or update) of the lexicondata 316, the domain-specific parsing rules 370, or a combinationthereof, may increase efficiency, robustness, and coverage, as comparedto manual generation (or update) of the lexicon data 316, thedomain-specific parsing rules 370, or both.

FIG. 4 illustrates a system 400 for performing lexically-driven parsing.The system 400 includes a device 402. The device 402 may include aprocessor, a computer, a laptop computer, a server, a communicationdevice, an entertainment device, or a combination thereof. The device402 may be the same as or distinct from the device 302 of FIG. 3.

The device 402 includes (or accesses) the domain-specificlexically-driven pre-parser 110. For example, the device 402 includes(or accesses) the text parser 304. The text parser 304 includes thedomain-specific lexically-driven pre-parser 110 and a domain-independentrule-based parser 412. The domain-independent rule-based parser 412 isconfigured to perform domain-independent parsing based on thedomain-independent parsing rules 470. The domain-independent parsingrules 470 may be previously generated at the device 402, received by thedevice 402 from a user, received by the device 402 from another device,or a combination thereof.

In a particular aspect, the device 402 may correspond to one or more ofthe cloud computing nodes 10 of FIG. 1. For example, the device 402 mayprovide the domain-specific lexically-driven pre-parser 110 (e.g.,software corresponding to the domain-specific lexically-drivenpre-parser 110) or functions of the domain-specific lexically-drivenpre-parser 110 as a service. In an alternate aspect, the device 402 maycorrespond to a cloud consumer device, such as, for example, thepersonal digital assistant (PDA) or cellular telephone 54A, the desktopcomputer 54B, the laptop computer 54C, the automobile computer system54N of FIG. 1, or a combination thereof. The device 402 may receive thedomain-specific lexically-driven pre-parser 110 (e.g., softwarecorresponding to the domain-specific lexically-driven pre-parser 110) oraccess functions of the domain-specific lexically-driven pre-parser 110as a service provided by one or more of the cloud computing nodes 10 ofFIG. 1.

The device 402 may include a memory 406. The memory 406 is configured tostore the lexicon data 316. In a particular aspect, the device 402 mayreceive the lexicon data 316, the domain-specific parsing rules 370, ora combination thereof, from another device, such as the device 302 ofFIG. 3. The device 402 may store the lexicon data 316 in the memory 406.

During operation, the text parser 304 may determine that input text 414is to be analyzed. For example, the text parser 304 may receive a userinput 484 from a user 401 or a request from another device indicatingthat the input text 414 is to be analyzed. The user 401 may be the sameas or distinct from the user 301 of FIG. 3. The text parser 304 maydetermine that the input text 414 is associated with the domain 320. Forexample, the text parser 304 may determine that the input text 414indicates the domain 320. To illustrate, a first line of the input text414 may include an identifier (e.g., “#medicine”) of the domain 320. Inan alternate aspect, the text parser 304 may receive the user input 484or data from another device indicating that the input text 414 isassociated with the domain 320. For example, the user input 484 (or thedata) may include an identifier of the input text 414 (e.g., a fileidentifier) and an identifier (e.g., “#medicine”) of the domain 320.

The text parser 304 may provide the input text 414 to thedomain-specific lexically-driven pre-parser 110 in response todetermining that the input text 414 is associated with the domain 320.The domain-specific lexically-driven pre-parser 110 may generatepartially parsed and bracketed input text 480 by processing the inputtext 414 based on the domain-specific parsing rules 370. For example,the input text 414 may include a sentence (e.g., “The patient suffersfrom high blood cholesterol”). In a particular aspect, thedomain-specific lexically-driven pre-parser 110 may copy the sentence togenerate an initial version (e.g., “The patient suffers from high bloodcholesterol”) of the partially parsed and bracketed input text 480. Thedomain-specific lexically-driven pre-parser 110 may update the partiallyparsed and bracketed input text 480 at various stages of processing. Forexample, at a particular stage of processing, the domain-specificlexically-driven pre-parser 110 may generate a next version of thepartially parsed and bracketed input text 480 by adding one or morephrase markers to a previous version of the partially parsed andbracketed input text 480, as described herein. The domain-specificlexically-driven pre-parser 110, in response to determining that thepre-parsing of the input text 414 is complete, provides the mostrecently generated version (e.g., “The patient suffers from [[_(ADJ)high] [[_(N) blood] [_(N) cholesterol]]]”) of the partially parsed andbracketed input text 480 to the domain-independent rule-based parser412, as described herein.

The domain-specific lexically-driven pre-parser 110 may identify terms(e.g., words) in the input text 414. For example, the domain-specificlexically-driven pre-parser 110 may determine that the input text 414includes a term (e.g., “The”), a term (e.g., “patient”), a term (e.g.,“suffers”), a term (e.g., “from”), a term 422 (e.g., “high”), a term 424(e.g., “blood”), and a term 426 (e.g., “cholesterol”).

The domain-specific lexically-driven pre-parser 110 may determine thatthe term 426 (e.g., cholesterol) is indicated as the base term 322 inthe entry 318 of the lexicon data 316. The domain-specificlexically-driven pre-parser 110 may, in response to determining that thecore data 330 indicates a part of speech (e.g., noun) of the base term322, update the partially parsed and bracketed input text 480 (e.g.,“The patient suffers from high blood cholesterol”) to indicate the partof speech of the term 426 corresponding to the base term 322. Forexample, the domain-specific lexically-driven pre-parser 110 may add aphrase marker (e.g., [_(N)]) around the term 426 (e.g., cholesterol) inthe partially parsed and bracketed input text 480 (e.g., “The patientsuffers from high blood [_(N) cholesterol]”) to indicate the part ofspeech (e.g., noun).

The domain-specific lexically-driven pre-parser 110 may determine thatthe term 424 (e.g., blood) is a potential modifier term of the term 426(e.g., cholesterol) in response to determining that the term 424 appearsto modify (e.g., is next to) the term 426 in the input text 414. Thedomain-specific lexically-driven pre-parser 110 may compare a potentialmodifier term (e.g., the term 424) to modifier terms indicated in thenon-core data 340. The domain-specific lexically-driven pre-parser 110may determine that the term 424 (e.g., blood) is indicated as themodifier term 324 (e.g., nominal pre-modifier term) in the non-core data340.

The domain-specific lexically-driven pre-parser 110 may determinewhether the modifier term 324 is associated with a collocation rule. Forexample, the domain-specific lexically-driven pre-parser 110 maydetermine whether the modifier term 324 is of a modifier type thatindicates a collocation rule. A pre-modifier term may indicate a firstcollocation rule (e.g., left attachment). A post-modifier term mayindicate a second collocation rule (e.g., right attachment). Thedomain-specific lexically-driven pre-parser 110 may determine that themodifier term 324 is associated with a particular collocation rule(e.g., left attachment) in response to determining that the modifierterm 324 is of a modifier type (e.g., a pre-modifier term) thatindicates the particular collocation rule. Alternatively, or inaddition, the domain-specific lexically-driven pre-parser 110 maydetermine that the modifier term 324 is associated with the particularcollocation rule in response to determining that the domain-specificparsing rules 370 indicate that the modifier type (e.g., a pre-modifierterm) is associated with the particular collocation rule (e.g., leftattachment of pre-modifier terms).

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the modifier term 324 is associated with a particularcollocation rule (e.g., left attachment), determine whether a positionof the term 424 (e.g., blood) relative to the term 426 (e.g.,cholesterol) in the input text 414 satisfies the particular collocationrule (e.g., left attachment). For example, the domain-specificlexically-driven pre-parser 110 may, in response to determining that theterm 424 (e.g., blood) is prior to (e.g., on the left of) the term 426(e.g., cholesterol) in the input text 414, determine that the term 424(e.g., blood) satisfies the first collocation rule (e.g., leftattachment) associated with the modifier term 324 (e.g., nominalpre-modifier term). The domain-specific lexically-driven pre-parser 110may, in response to determining that the term 424 (e.g., blood)satisfies the collocation rule (e.g., left attachment) associated withthe modifier term 324, update the partially parsed and bracketed inputtext 480 (e.g., “The patient suffers from high [[_(N) blood] [_(N)cholesterol]]”) by adding a phrase marker (e.g., [_(N)]) around the term424 and by bracketing (e.g., grouping) the term 424 (e.g., blood) withthe term 426 (e.g., cholesterol). The phrase marker (e.g., [_(N)])around the term 424 may indicate a part of speech (e.g., noun)corresponding to the modifier type (e.g., nominal modifier term) of themodifier term 324 (e.g., blood).

The domain-specific lexically-driven pre-parser 110 may determine thatthe term 422 (e.g., high) is a potential modifier term of the term 426(e.g., cholesterol) in response to determining that the term 422 appearsto modify the term 426 in the input text 414. For example, thedomain-specific lexically-driven pre-parser 110 may determine that theterm 422 appears to modify the term 426 in response to determining thatthe term 422 is next to the term 424 that is bracketed (e.g., grouped)with the term 426 in the partially parsed and bracketed input text 480(e.g., “The patient suffers from high [[_(N) blood] [_(N)cholesterol]]”.

The domain-specific lexically-driven pre-parser 110 may compare apotential modifier term (e.g., the term 422) to modifier terms indicatedby the non-core data 340. The domain-specific lexically-drivenpre-parser 110 may determine that the term 422 (e.g., high) is indicatedas the modifier term 334 (e.g., adjectival modifier term) in thenon-core data 340.

The domain-specific lexically-driven pre-parser 110 may determinewhether the modifier term 334 is associated with a collocation rule. Forexample, the domain-specific lexically-driven pre-parser 110 may, inresponse to determining that the non-core data 340 is silent regarding(e.g., does not indicate) whether the modifier term 334 is apre-modifier term or a post-modifier term, determine whether thedomain-specific parsing rules 370 indicate a collocation rule associatedwith a modifier type (e.g., adjectival modifier term) of the modifierterm 334 (e.g., high). To illustrate, the domain-specificlexically-driven pre-parser 110 may determine that the modifier term 334(e.g., high) is associated with a particular collocation rule (e.g.,left attachment) in response to determining that the domain-specificparsing rules 370 indicate that the modifier type (e.g., adjectivalmodifier term) is associated with the particular collocation rule.Alternatively, the domain-specific lexically-driven pre-parser 110 may,in response to determining that neither the non-core data 340 nor thedomain-specific parsing rules 370 indicate a collocation rule associatedwith the modifier term 334 (e.g., high), determine that a defaultcollocation rule (e.g., left attachment) is associated with the modifierterm 334.

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the modifier term 334 (e.g., high) is associated with aparticular collocation rule (e.g., left attachment), determine whether aposition of the term 422 (e.g., high) relative to the term (e.g.,cholesterol) in the input text 414 satisfies the particular collocationrule. For example, the domain-specific lexically-driven pre-parser 110may, in response to determining that the term 422 (e.g., high) is priorto (e.g., on the left of) the term 426 (e.g., cholesterol) in the inputtext 414, the partially parsed and bracketed input text 480, or both.

In a particular aspect, the domain-specific lexically-driven pre-parser110 may determine that a first term satisfies a first collocation rule(e.g., left attachment) relative to the term 426 in response todetermining that the first term is prior to (or on the left) of a secondterm that is bracketed (i.e., grouped) with the term 426 in thepartially parsed and bracketed input text 480. For example, thedomain-specific lexically-driven pre-parser 110 may determine that theterm 422 (e.g., high) satisfies the first collocation rule (e.g., leftattachment) in response to determining that the term 422 (e.g., high)occurs prior to (e.g., on the left of) the term 424 (e.g., blood) thatis bracketed with the term 426 (e.g., cholesterol) in the partiallyparsed and bracketed input text 480 (e.g., “The patient suffers fromhigh [[_(N) blood] [_(N) cholesterol]]”).

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the term 422 (e.g., high) satisfies the firstcollocation rule (e.g., left attachment), update the partially parsedand bracketed input text 480 (e.g., “The patient suffers from [[_(ADJ)high] [[_(N) blood] [_(N) cholesterol]]]”) adding a phrase marker (e.g.,[_(ADJ)]) and by bracketing (e.g., grouping) the term 422 (e.g., high)with the bracketed (i.e., grouped) terms including the term 426 (e.g.,cholesterol).

In a particular aspect, the domain-specific lexically-driven pre-parser110 may determine that a term (e.g., from) is a potential modifier termof the term 426 (e.g., cholesterol) in response to determining that theterm (e.g., from) appears to modify the term 426 in the input text 414.For example, the domain-specific lexically-driven pre-parser 110 maydetermine that the term (e.g., from) appears to modify the term 426 inresponse to determining that the term (e.g., from) is next to the term422 (e.g., high) that is bracketed (e.g., grouped) with the term 426 inthe partially parsed and bracketed input text 480 (e.g., “The patientsuffers from [[_(ADJ) high] [[_(N) blood] [_(N) cholesterol]]]”). A“potential modifier term” may or may not be a modifier term of the term426. As used herein, a “potential modifier term” of the term 426includes a term that is syntactically linked to the term 426. Thedomain-specific lexically-driven pre-parser 110 may determine that theterm (e.g., from) is syntactically linked to the term 422 (e.g., high)in response to determining that the term (e.g., from) is next to theterm 422 (e.g., high) that is bracketed (e.g., grouped) with the term426 in the partially parsed and bracketed input text 480 (e.g., “Thepatient suffers from [[_(ADJ) high] [[_(N) blood] [_(N)cholesterol]]]”).

The domain-specific lexically-driven pre-parser 110 may determinewhether the potential modifier term (e.g., from) is in fact a modifierterm of the term 426. For example, the domain-specific lexically-drivenpre-parser 110 may compare the potential modifier term (e.g., from) tomodifier terms indicated by the non-core data 340. The domain-specificlexically-driven pre-parser 110 may, in response to determining that thepotential modifier term (e.g., from) is not indicated as a modifier termby the non-core data 340, determine that the input text 414 does notinclude any additional domain-specific modifier terms of the base term322 that are prior to the term 426.

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the input text 414 does not include any additionaldomain-specific modifier terms prior to the term 426, determine whetherthe input text 414 includes potential modifier terms subsequent to theterm 426. The domain-specific lexically-driven pre-parser 110 may, inresponse to determining that the input text 414 does not include anyadditional domain-specific modifier terms prior to the term 426 orsubsequent to the term 426, determine that there is no additionalmodifier term associated with the term 426 to be identified.

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that there are no additional modifier terms to be identifiedfor the term 426, determine whether the input text 414 includes anotherterm that is indicated as a base term by the lexicon data 316. Thedomain-specific lexically-driven pre-parser 110 may, in response todetermining that the input text 414 includes another term that isindicated as a base term, determine modifier terms of the other term inthe input text 414, as described herein. Alternatively, thedomain-specific lexically-driven pre-parser 110, in response todetermining that the input text 414 does not include another term thatis indicated as a base term by the lexicon data 316, may determine thatpre-parsing of the input text 414 is complete.

In a particular aspect, the domain-specific lexically-driven pre-parser110 may generate (or update) the partially parsed and bracketed inputtext 480 in response to determining that the input text 414 satisfies atleast one of a morpho-semantic rule, a named-entity-based pattern rule,or a semantico-syntactic pattern rule of the domain-specific parsingrules 370, as further described with reference to FIG. 8. Thedomain-specific lexically-driven pre-parser 110 may determine that thepre-parsing of the input text 414 is complete in response to determiningthat no additional rules (or none) of the domain-specific parsing rules370 are applicable to the partially parsed and bracketed input text 480.

It should be understood that iteratively updating the partially parsedand bracketed input text 480 is described as an illustrative,non-limiting, example. In an alternative aspect, the domain-specificlexically-driven pre-parser 110 copies the sentence of the input text414 to the memory 406 as an initial version (e.g., “The patient suffersfrom high blood cholesterol”) of processing data. The domain-specificlexically-driven pre-parser 110 updates the processing data at variousstages of processing. For example, the domain-specific lexically-drivenpre-parser 110 generates a next version of the processing data byadding, based on the lexicon data 316 and the domain-specific parsingrules 370, one or more phrase markers to a previous version of theprocessing data. In this aspect, the domain-specific lexically drivenpre-parser 110, in response to determining that pre-parsing of the inputtext 414 is complete, designates the most recently generated version(e.g., “The patient suffers from [[_(ADJ) high] [[_(N) blood] [_(N)cholesterol]]]”) of the processing data as the partially parsed andbracketed input text 480.

The partially parsed and bracketed input text 480 may be prepared forprocessing (e.g., parsing) by the domain-independent rule-based parser412. The domain-specific lexically-driven pre-parser 110 may, inresponse to determining that the pre-parsing of the input text 414 iscomplete, provide the partially parsed and bracketed input text 480(e.g., “The patient suffers from [[_(ADJ) high] [[_(N) blood] [_(N)cholesterol]]]”) to the domain-independent rule-based parser 412.

The domain-independent rule-based parser 412 may process the partiallyparsed and bracketed input text 480 (e.g., “The patient suffers from[[_(ADJ) high] [[_(N) blood] [_(N) cholesterol]]]”) based on thedomain-independent parsing rules 470 to generate parsed text 482, asfurther described with reference to FIG. 5. The parsed text 482 may beassociated with the domain 320. The domain-independent rule-based parser412 may provide a message to a display of the device 402 that the inputtext 414 has been successfully parsed based at least in part on thedomain-specific parsing rules 370 associated with the domain 320. Forexample, the message may indicate that the input text 414 has beenparsed by the domain-independent rule-based parser 412 using thedomain-specific parsing rules 370.

The domain-independent parsing rules 470 may be maintained (e.g.,updated) independently of the domain-specific parsing rules 370. Thedomain-independent rule-based parser 412 may be configured to receivepartially parsed and bracketed text from multiple domain-specificlexically-driven pre-parsers. For example, the domain-independentrule-based parser 412 may be configured to receive the partially parsedand bracketed input text 480 generated by the domain-specificlexically-driven pre-parser 110 and to receive second partially parsedand bracketed text generated by a second domain-specificlexically-driven pre-parser. The domain 320 associated with thedomain-specific lexically-driven pre-parser 110 may be distinct from asecond domain associated with the second domain-specificlexically-driven pre-parser. In a particular aspect, a domain associatedwith the input text 414 may be unknown to the device 402. The textparser 304 may provide the input text 414 to multiple domain-specificlexically-driven pre-parsers (e.g., the domain-specific lexically-drivenpre-parser 110 and the second domain-specific lexically-drivenpre-parser 110). The text parser 304 may identify a domain associatedwith the input text 414 based on determining whether the partiallyparsed and bracketed input text 480, the second partially parsed andbracketed text, or both, are successfully parsed by thedomain-independent rule-based parser 412. For example, the text parser304 may determine that the input text 414 is likely associated with thedomain 320, the second domain, or both, in response to determining thatthe partially parsed and bracketed input text 480, the second partiallyparsed and bracketed text, or both, respectively, are parsedsuccessfully by the domain-independent rule-based parser 412.

In a particular aspect, the parsed text 482 may be processed by anothercomponent of the device 402 or by another device. For example, the inputtext 414 may correspond to doctor notes. A hospital record component(e.g., processor) of the device 402 may update patient-care records(e.g., a database) based on the parsed text 482, the user input 484, orboth. As another example, the input text 414 may correspond to researchpapers. A research system (e.g., a processor) may update a research databased on the parsed text 482.

The system 400 enables parsing of the input text 414 based on thedomain-specific parsing rules 370, the domain-independent parsing rules470, or a combination thereof. Having distinct domain-specific parsingrules may improve performance. For example, specialized domains (e.g.,the domain 320) may introduce syntactic patterns and may present withsyntactic ambiguity types that are less common in the general domain.Pre-parsing input text of the specialized domains (e.g., the domain 320)based on the domain-specific parsing rules 370 may reduce (e.g.,resolve) syntactic ambiguities prior to parsing based on thedomain-independent parsing rules 470, thereby resulting in fewer (e.g.,no) parsing errors.

FIG. 5 illustrates an intermediate parse tree 580 and a parse tree 582.The intermediate parse tree 580 may be generated by the domain-specificlexically-driven pre-parser 110 of FIG. 1, the text parser 304 of FIG.3, the device 402, the system 400 of FIG. 4, or a combination thereof.The intermediate parse tree 580 may correspond to (e.g., represent) thepartially parsed and bracketed input text 480 (e.g., “The patientsuffers from [[_(ADJ) high] [[_(N) blood] [_(N) cholesterol]]]”).

The parse tree 582 may be generated by the text parser 304 of FIG. 3,the domain-independent rule-based parser 412, the device 402, the system400 of FIG. 4, or a combination thereof. For example, thedomain-independent rule-based parser 412 may generate the parsed text482 by parsing the partially parsed and bracketed input text 480 basedon the domain-independent parsing rules 470, as described herein. Theparse tree 582 may correspond to (e.g., represent) the parsed text 482.

The domain-independent parsing rules 470 may include the followingrules:

S→NP VP VP→V PP PP→PREP NP NP→DET NOM NP→NOM NOM→N NOM→N NOM NOM→ADJ NOMDET→“The” N→“patient” V→“suffers” PREP→“from”

where S corresponds to a sentence, NP corresponds to a noun phrase, VPcorresponds to a verb phrase, V corresponds to a verb, PP corresponds toa prepositional phrase, PREP corresponds to a preposition, DETcorresponds to a determiner, NOM corresponds to a nominal, N correspondsto a noun, and ADJ corresponds to an adjective.

The domain-independent rule-based parser 412 may generate the parsedtext 482 by parsing the partially parsed and bracketed input text 480(e.g., “The patient suffers from [[_(ADJ) high] [[_(N) blood] [_(N)cholesterol]]]”) based on the domain-independent parsing rules 470. Forexample, the domain-independent rule-based parser 412 may generate theparsed text 482 by copying the partially parsed and bracketed input text480 (e.g., “The patient suffers from [[_(ADJ) high] [[_(N) blood] [_(N)cholesterol]]]”).

The domain-independent rule-based parser 412 may, subsequent togenerating the parsed text 482 by copying the partially parsed andbracketed input text 480, update the parsed text 482 based on applyingvarious rules of the domain-independent parsing rules 470. For example,the domain-independent rule-based parser 412 may, in response todetermining that a term 514 (e.g., “The”) of the partially parsed andbracketed input text 480 corresponds to a part of speech (e.g., DET)based on a rule (e.g., DET→“The”) of the domain-independent parsingrules 470, update the parsed text 482 (e.g., “[_(DET) The] patientsuffers from [[_(ADJ) high] [[_(N) blood] [_(N) cholesterol]]]”) byadding a phrase marker (e.g., [_(DET)]) around the term 514 (e.g.,“The”). The domain-independent rule-based parser 412 may continueapplying various rules of the domain-independent parsing rules 470 togenerate the parsed text 482 (e.g., “[_(S) [_(NP) [_(DET) The] [_(NOM)[_(N) patient]]] [_(VP) [_(V) suffers] [_(PP) [_(PREP) from] [_(NP)[_(NOM) [_(ADJ) high] [_(NOM) [_(N) blood] [_(NOM) [_(N)cholesterol]]]]]]]]”). The domain-independent rule-based parser 412 maydetermine that parsing of the partially parsed and bracketed input text480 is successful in response to determining that the parsed text 482includes a particular phrase marker (e.g., [_(S)]).

The input text 414 may include a syntactic ambiguity. For example, theterm 422 (e.g., “high”) may be a potential modifier of each of the term424 (e.g., “blood”) and the term 426 (e.g., “cholesterol”). The lexicondata 316 may indicate the term 426 (e.g., “cholesterol”) as the baseterm 322 and the non-core data 340 may indicate the term 422 (e.g.,“high”) as the modifier term 334 of the base term 322. The lexicon data316 may include a second entry indicating the term 424 as a second baseterm. The second entry may include second non-core data indicating oneor more modifier terms of the second base term. The term 422 (e.g.,“high”) may be absent from the one or more modifier terms of the secondbase term (e.g., “blood”). The domain-specific lexically-drivenpre-parser 110 may refrain from grouping the term 422 (e.g., “high”)with the base term (e.g., “blood”) in response to determining that theterm 422 (e.g., “high”) is absent from the one or more modifier terms ofthe second base term (e.g., “blood”).

The domain-specific lexically-driven pre-parser 110 may group (e.g.,bracket) the term 424 (e.g., “blood”) and the term 426 (e.g.,“cholesterol”) to generate a first grouped term (e.g., “[bloodcholesterol]”), and may group the term 422 (e.g., “high”) with the firstgrouped term (e.g., “[blood cholesterol]”) to generate a second groupedterm (e.g., “[high [blood cholesterol]]”), as described with referenceto FIG. 4. The second grouped term (e.g., “[high [blood cholesterol]]”)of the partially parsed and bracketed input text 480 (e.g., “The patientsuffers from [[_(ADJ) high] [[_(N) blood] [_(N) cholesterol]]]”) mayresolve the syntactic ambiguity by indicating that the term 422 modifiesthe first grouped term (e.g., “[blood cholesterol]”). Consequently, thedomain-independent rule-based parser 412 may have a higher likelihood ofsuccessfully parsing the partially parsed and bracketed input text 480.

FIG. 6 illustrates entries 600 of the lexicon data 316 of FIG. 3. Theentries 600 may be generated by the lexical analyzer 108 of FIG. 1, thedevice 302, the system 300 of FIG. 3, or a combination thereof.

The entries 600 may be associated with the domain 320 of FIG. 3. Forexample, the lexical analyzer 108 may generate (or update) the entries600 based on analyzing the domain-specific corpus 314, as described withreference to FIG. 3. In a particular aspect, the entries 600 may bebased on an analysis of multiple domain-specific texts (e.g., documents)associated with the domain 320. For example, the lexical analyzer 108may generate (or update) some of the entries 600 based on analyzing afirst domain-specific text of the domain-specific corpus 314 and some ofthe entries 600 based on analyzing another domain-specific text of thedomain-specific corpus 314.

The entries 600 include an entry 602, an entry 604, an entry 606, and anentry 608. It should be understood that four entries are used herein asillustrative examples. The lexicon data 316 may include four entries,fewer than four entries, or more than four entries.

The entry 602 indicates a base term 622 (e.g., “edema, oedema”). Theentry 602 includes alternative spellings of the base term 622. Forexample, base term 622 may have a first spelling (e.g., “edema”) and asecond spelling (e.g., “oedema”). Each of the alternative spellings maybe valid in the domain 320. The entry 602 includes core data 642 andnon-core data 662 associated with the base term 622. The core data 642and the non-core data 662 indicate domain-independent anddomain-specific information, respectively.

The entry 604 indicates a base term 624 (e.g., “hypertension”). Theentry 604 includes core data 644 and non-core data 664 associated withthe base term 624. The entry 606 indicates a base term 626 (e.g.,“extremity”). The entry 606 includes core data 646 and non-core data 667associated with the base term 626. The entry 608 indicates a base term628 (e.g., “triglyceride”). The entry 608 includes core data 648 andnon-core data 668 associated with the base term 628. The domain-specificlexically-driven pre-parser 110 may process input text based on one ormore of the entries 602-608, as further described with reference to FIG.8.

FIG. 7 illustrates examples of the domain-specific parsing rules 370.The domain-specific parsing rules 370 may be generated by the lexicalanalyzer 108 of FIG. 1, the device 302, the system 300 of FIG. 3, or acombination thereof. For example, the lexical analyzer 108 may generatethe domain-specific parsing rules 370 based on the domain-specificcorpus 314, the user input 382, or both, as described herein.

The domain-specific parsing rules 370 may include a collocation rule 702(e.g., right attachment of prepositional phrases). The collocation rule702 may indicate that a preposition modifier term subsequent to (e.g.,on the right of) a corresponding base term is valid in the domain 320.The lexical analyzer 108 may generate the collocation rule 702 inresponse to determining that at least a threshold number ofprepositional terms are detected subsequent to (e.g., on the right of)corresponding base terms in the domain-specific corpus 314. For example,the lexical analyzer 108 may generate the collocation rule 702 based atleast in part on determining that the modifier term 344 (e.g., “in”) ofFIG. 3 is detected subsequent to (e.g., on the right of) the base term322 (e.g., “cholesterol”) in the domain-specific corpus 314.Alternatively, the lexical analyzer 108 may generate the collocationrule 702 in response to determining that the user input 382 indicatesthat a preposition modifier term subsequent to (e.g., on the right of) acorresponding base term is valid in the domain 320.

The domain-specific parsing rules 370 may include a collocation rule 704(e.g., left attachment of adjectival phrases). The collocation rule 704may indicate that an adjectival modifier term prior to (e.g., on theleft of) a corresponding base term is valid in the domain 320. Thelexical analyzer 108 may generate the collocation rule 704 in responseto determining that at least a threshold number of adjectival terms aredetected prior to (e.g., on the left of) corresponding base terms in thedomain-specific corpus 314. For example, the lexical analyzer 108 maygenerate the collocation rule 704 based at least in part on determiningthat the modifier term 334 (e.g., “high”) is detected prior to (e.g., onthe left of) the base term 322 (e.g., “cholesterol”) in thedomain-specific corpus 314. Alternatively, the lexical analyzer 108 maygenerate the collocation rule 704 in response to determining that theuser input 382 indicates that an adjectival modifier term prior to(e.g., on the left of) a corresponding base term is valid in the domain320.

The domain-specific parsing rules 370 may include a morpho-semantic rule706 (e.g., Tokens with semantic features {low, high, elevated} < >Prefix [HYPER]). The morpho-semantic rule 706 may indicate that termshaving particular semantic features (e.g., low, high, elevated) are notvalid modifier terms of a base term with a particular prefix (e.g.,“hyper”) in the domain 320.

In a particular aspect, the lexical analyzer 108 may generate themorpho-semantic rule 706 based on analyzing the domain-specific corpus314. For example, the lexical analyzer 108 may determine that a firstnumber (e.g., 0) of modifier terms having the particular semanticfeatures are detected prior to (e.g., on the left of) corresponding baseterms having the particular prefix (e.g., “hyper”) in at least a portionof the domain-specific corpus 314. The lexical analyzer 108 may generatethe morpho-semantic rule 706 in response to determining that the firstnumber (e.g., 0) is less than or equal to a threshold. In a particularaspect, the lexical analyzer 108 may determine the first number (e.g.,0) of the modifier terms in response to determining that a configurationsetting, the user input 382, data from another device, or a combinationthereof, indicate that the relationship between the particular prefixand terms having the particular semantic features is to be evaluated.Alternatively, the lexical analyzer 108 may generate the morpho-semanticrule 706 in response to determining that the user input 382 indicatesthat terms having particular semantic features (e.g., low, high,elevated) are not valid modifier terms of a base term with a particularprefix (e.g., “hyper”) in the domain 320.

The domain-specific parsing rules 370 may include a named-entity-basedpattern rule 708 (e.g., “Release of V from W by X at Y with Z”). Thenamed-entity-based pattern rule 708 may indicate a particular pattern ofterms that includes one or more named-entities (e.g., V, W, X, Y, andZ). For example, a first named-entity (e.g., V) may correspond to afirst semantic type (e.g., person_name), a second named-entity (e.g., W)may correspond to a second semantic type (e.g., department_name), athird named-entity (e.g., X) may correspond to a third semantic type(e.g., person_name), a fourth named-entity (Y) may correspond to afourth semantic type (e.g., time), and a fifth named-entity (Z) maycorrespond to a fifth semantic type (e.g., person_name).

In a particular aspect, the lexical analyzer 108 may generate thenamed-entity-based pattern rule 708 based on analyzing thedomain-specific corpus 314. For example, the lexical analyzer 108 maydetermine, based on named-entity-based pattern detection techniques,that the particular pattern occurs a first number of times (e.g., 5) inat least a portion of the domain-specific corpus 314. The lexicalanalyzer 108 may generate the named-entity-based pattern rule 708 inresponse to determining that the first number of times (e.g., 5) isgreater than or equal to a threshold (e.g., 2). In a particular aspect,the lexical analyzer 108 may determine the first number of times (e.g.,5) in response to determining that a configuration setting, the userinput 382, data from another device, or a combination thereof, indicatethat named-entity-based pattern detection is to be performed.Alternatively, the lexical analyzer 108 may generate thenamed-entity-based pattern rule 708 in response to determining that theuser input 382 indicates that the particular named-entity-based pattern(e.g., “Release of V from W by X at Y with Z”) is valid in the domain320.

The domain-specific parsing rules 370 may include a semantico-syntacticpattern rule 710 (e.g., [ACTION] [PREP] {substance | drug} [PREP]{Agent} [PREP]{Location | Measure}). The semantico-syntactic patternrule 710 may indicate a particular pattern of terms, where the patternindicates phrase types and semantic types of one or more terms. Forexample, the semantico-syntactic pattern rule 710 (e.g., [ACTION] [PREP]{substance | drug} [PREP] {Agent} [PREP] {Location | Measure}) mayindicate that an action phrase (e.g., “prescribing”) followed by a firstpreposition (e.g., “of”) followed by a first term (e.g.,“acetaminophen”) having a first semantic type (e.g., substance or drug)followed by a second preposition (e.g., “by”) followed by a second term(e.g., person's name) having a second semantic type (e.g., agent)followed by a third preposition (e.g., “at” or “of”) followed by a thirdterm (e.g., “clinic” or “10 doses”) having a third semantic type (e.g.,location or measure) is valid in the domain 320.

In a particular aspect, the lexical analyzer 108 may generate thesemantico-syntactic pattern rule 710 based on analyzing thedomain-specific corpus 314. For example, the lexical analyzer 108 maydetermine, based on semantico-syntactic pattern detection techniques,that the particular pattern occurs a first number of times (e.g., 3) inat least a portion of the domain-specific corpus 314. The lexicalanalyzer 108 may generate the semantico-syntactic pattern rule 710 inresponse to determining that the first number of times (e.g., 3) isgreater than or equal to a threshold (e.g., 2). In a particular aspect,the lexical analyzer 108 may determine the first number of times (e.g.,3) in response to determining that a configuration setting, the userinput 382, data from another device, or a combination thereof, indicatethat semantico-syntactic pattern detection is to be performed.Alternatively, the lexical analyzer 108 may generate thesemantico-syntactic pattern rule 710 in response to determining that theuser input 382 indicates that the particular semantico-syntactic pattern(e.g., [ACTION] [PREP] {substance | drug} [PREP] {Agent} [PREP]{Location | Measure}) is valid in the domain 320. The domain-specificlexically-driven pre-parser 110 may process input text based on one ormore of the domain-specific parsing rules 370, as further described withreference to FIG. 8.

FIG. 8 illustrates examples 800 of input text and correspondingpartially parsed and bracketed input text. The examples 800 includeinput text 802, input text 804, input text 806, and input text 810.

The domain-specific lexically-driven pre-parser 110 may generatepartially parsed and bracketed input text 882 by processing the inputtext 802 (e.g., “The patient suffers from high cholesterol,triglycerides, and hypertension.”), as described with reference to FIG.4. The domain-specific lexically-driven pre-parser 110 may generate thepartially parsed and bracketed input text 882 based at least in part onthe entry 318 associated with the base term 322 (e.g., “cholesterol”),the entry 604 associated with the base term 624 (e.g., “hypertension”),the entry 608 associated with the base term 628 (e.g., “triglyceride”),the collocation rule 704, and the morpho-semantic rule 706 (e.g., Tokenswith semantic features {low, high, elevated} < > Prefix [HYPER]), asdescribed herein.

The domain-specific lexically-driven pre-parser 110 may determine thatthe modifier term 334 (“high”) appears to modify the base term 322(e.g., “cholesterol”) in the input text 802. The domain-specific parsingrules 370 may include one or more list rules (e.g., LIST→LIST CONJ N,LIST→N COMMA LIST, LIST→N, where COMMA corresponds to “,” and CONJcorresponds to “and”). The domain-specific lexically-driven pre-parser110 may determine, based on the one or more list rules, that base term322 (e.g., cholesterol) is included in a first list (e.g., “cholesterol,triglycerides, and hypertension”), a second list (e.g., “cholesterol,triglycerides”), and a third list (e.g., “cholesterol”).

The domain-specific parsing rules 370 may determine, based on thecollocation rule 704 (e.g., left attachment of adjectival phrases), thatthe modifier term 334 (e.g., “high”) could be bracketed with the firstlist, the second list, or the third list, to generate first text (e.g.,“The patient suffers from [high [cholesterol, triglycerides, andhypertension]].”), second text (e.g., “The patient suffers from [high[cholesterol, triglycerides]], and hypertension.”), or third text (e.g.,“The patient suffers from [high [cholesterol]], triglycerides, andhypertension.”), respectively.

The domain-specific lexically-driven pre-parser 110 may resolve theambiguity based on the entry 318 associated with the base term 322(e.g., “cholesterol”), the entry 604 associated with the base term 624(e.g., “hypertension”), the entry 608 associated with the base term 628(e.g., “triglyceride”), and the morpho-semantic rule 706 (e.g., Tokenswith semantic features {low, high, elevated} < > Prefix [HYPER]). Forexample, the domain-specific lexically-driven pre-parser 110 maydetermine that the morpho-semantic rule 706 (e.g., Tokens with semanticfeatures {low, high, elevated} < > Prefix [HYPER]) indicates that termshaving particular semantic features (e.g., low, high, elevated) areinvalid modifier terms of a base term with a particular prefix (e.g.,“hyper”) in the domain 320. The domain-specific lexically-drivenpre-parser 110 may determine that the first text (e.g., “The patientsuffers from [high [cholesterol, triglycerides, and hypertension]].”) isinvalid in the domain 320 in response to determining that the base term624 (e.g., “hypertension”) has a particular prefix (e.g., “hyper”) andthat the modifier term 334 (e.g., “high”) has a semantic feature (e.g.,high) that is indicated as an invalid modifier term for base termshaving the particular prefix.

The domain-specific lexically-driven pre-parser 110 may determine thatthe second text (e.g., “The patient suffers from [high [cholesterol,triglycerides]], and hypertension.”) and the third text (e.g., “Thepatient suffers from [high [cholesterol]], triglycerides, andhypertension.”) are valid in the domain 320 in response to determiningthat the non-core data 340 indicates the modifier term 334 (e.g.,“high”) as a valid modifier term for the base term 322 (e.g.,“cholesterol”) and that the non-core data 668 indicates the modifierterm 334 (e.g., “high”) as a valid modifier term for the base term 628(e.g., “triglyceride”).

The domain-specific lexically-driven pre-parser 110 may select one ofthe second text or the third text as the partially parsed and bracketedinput text 882. For example, the domain-specific lexically-drivenpre-parser 110 may select the second text in response to determiningthat a greater number of terms are grouped with the modifier term 334 inthe second text as compared to the third text. In a particular aspect,the domain-specific lexically-driven pre-parser 110 may select thesecond text in response to determining that the non-core data 668indicates that the modifier term 334 (e.g., high) is a preferreddomain-specific modifier for the base term 628 (e.g., “triglyceride”).The domain-specific lexically-driven pre-parser 110 may output thesecond text as the partially parsed and bracketed input text 882 (e.g.,“The patient suffers from [[_(ADJ) high] [[_(N) cholesterol], [_(N)triglycerides]]], and [_(N) hypertension].”).

The domain-specific lexically-driven pre-parser 110 may generatepartially parsed and bracketed input text 884 by processing the inputtext 804 (e.g., “The patient has a lower extremity edema.”), asdescribed with reference to FIG. 4. For example, the domain-specificlexically-driven pre-parser 110 may generate the partially parsed andbracketed input text 884 based at least in part on the entry 602associated with the base term 622 (e.g., “edema”), the entry 606associated with the base term 626 (e.g., “extremity”), and thecollocation rule 704, as described herein.

The domain-specific lexically-driven pre-parser 110 may determine thatthe input text 804 includes the base term 622 (e.g., “edema”) and thebase term 626 (e.g., “extremity”). The domain-specific lexically-drivenpre-parser 110 may determine that the base term 622 (e.g., “edema”)appears to modify the base term 626 (e.g., “extremity”), and vice versa,in the input text 804. The domain-specific lexically-driven pre-parser110 may determine that the non-core data 662 corresponding to the baseterm 622 (e.g., “edema”) is silent regarding whether (e.g., does notinclude) the base term 626 (e.g., “extremity”) is a valid modifier term.The domain-specific lexically-driven pre-parser 110 may determine thatthe non-core data 667 corresponding to the base term 626 (e.g.,“extremity”) is silent regarding whether (e.g., does not include) thebase term 622 (e.g., “edema”) is a valid modifier term. Thedomain-specific lexically-driven pre-parser 110 may refrain fromgrouping the base term 626 with the base term 622 in response todetermining that the non-core data 662 and the non-core data 667 aresilent regarding whether the base term 626 and the base term 622,respectively, are valid modifier terms for each other.

The domain-specific lexically-driven pre-parser 110 may determine that aterm (e.g., “lower”) appears to modify the base term 626 (e.g.,“extremity”) in the input text 804. The domain-specific lexically-drivenpre-parser 110 may, in response to determining that the term (e.g.,“lower”) is indicated as a valid modifier term in the non-core data 667corresponding to the base term 626 (e.g., “extremity”), group (e.g.,bracket) the term (e.g., “lower”) with the base term 626 (e.g.,“extremity”) to generate the partially parsed and bracketed input text884 (“The patient has a [[_(ADJ) lower] [_(N) extremity]] [_(N)edema].”)

The domain-specific lexically-driven pre-parser 110 may generatepartially parsed and bracketed input text 886 by processing the inputtext 806 (e.g., “Release of Mr. Shah from Emergency Room by Dr. Smith at2 PM with Mrs. Shah”), as described with reference to FIG. 4. Forexample, the domain-specific lexically-driven pre-parser 110 maygenerate the partially parsed and bracketed input text 886 based atleast in part on the named-entity-based pattern rule 708 (e.g., “Releaseof V from W by X at Y with Z”), as described herein.

The domain-specific lexically-driven pre-parser 110 may determine thatthe input text 886 satisfies the pattern indicated by thenamed-entity-based pattern rule 708. For example, the domain-specificlexically-driven pre-parser 110 may determine that the input text 886matches the pattern indicated by the named-entity-based pattern rule 708in response to determining that the input text 886 includes a first term(e.g., “Release”) followed by a second term (e.g., “of”) followed by oneor more terms (e.g., “Mr. Shah”) followed by a third term (e.g., “from”)followed by one or more terms (e.g., “Emergency Room”) followed by afourth term (e.g., “by”) followed by one or more terms (e.g., “Dr.Smith”) followed by a fifth term (e.g., “at”) followed by one or moreterms (e.g., “2 PM”) followed by a sixth term (e.g., “with”) followed byone or more terms (e.g., “Mrs. Shah”).

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the input text 886 matches the pattern indicated by thenamed-entity-based pattern rule 708, determine that the one or moreterms (e.g., “Mr. Shah”) between the second term (e.g., “of”) and thethird term (e.g., “from”) correspond to a first named-entity (e.g., V)associated with a first semantic type (e.g., person_name). Thedomain-specific lexically-driven pre-parser 110 may also determine thatthe one or more terms (e.g., “Emergency Room”) between the third term(e.g., “from”) and the fourth term (e.g., “by”) correspond to a secondnamed-entity (e.g., W) associated with a second semantic type (e.g.,department_name). The domain-specific lexically-driven pre-parser 110may determine that the one or more terms (e.g., “Dr. Smith”) between thefourth term (e.g., “by”) and the fifth term (e.g., “at”) correspond to athird named-entity (e.g., X) associated with a third semantic type(e.g., person_name). The domain-specific lexically-driven pre-parser 110may determine that the one or more terms (e.g., “2 PM”) between thefifth term (e.g., “at”) and the sixth term (e.g., “with”) correspond toa fourth named-entity (Y) associated with a fourth semantic type (e.g.,time). The domain-specific lexically-driven pre-parser 110 may determinethat the one or more terms (e.g., “Mrs. Smith”) following the sixth term(e.g., “with”) correspond to a fifth named-entity (Z) associated with afifth semantic type (e.g., person_name).

The domain-specific lexically-driven pre-parser 110 may generate thepartially parsed and bracketed input text 886 indicating the identifiednamed-entities. For example, the partially parsed bracketed input text886 (e.g., “Release of [_(V) Mr. Shah] from [_(W) Emergency Room] by[_(X) Dr. Smith] at [_(Y) 2 PM] with [_(Z) Mrs. Shah]”) may include aseparate phrase marker corresponding to each of the named-entities.

The domain-specific lexically-driven pre-parser 110 may generatepartially parsed and bracketed input text 888 by processing the inputtext 808 (e.g., “Prescribing of acetaminophen by Dr. Smith at EmergencyRoom”), as described with reference to FIG. 4. For example, thedomain-specific lexically-driven pre-parser 110 may generate thepartially parsed and bracketed input text 888 based at least in part onthe semantico-syntactic pattern rule 710 (e.g., [ACTION] [PREP]{substance | drug}[PREP] {Agent} [PREP] {Location | Measure}), asdescribed herein.

The domain-specific lexically-driven pre-parser 110 may determine thatthe input text 888 satisfies the pattern indicated by thesemantico-syntactic pattern rule 710. For example, the domain-specificlexically-driven pre-parser 110 may determine that the input text 888matches the pattern indicated by the semantico-syntactic pattern rule710 in response to determining that the input text 888 includes at leastone term corresponding to a semantic type indicated by thesemantico-syntactic pattern rule 710 in the order indicated by thesemantico-syntactic pattern rule 710. To illustrate, the domain-specificlexically-driven pre-parser 110 may determine that the input text 888includes a first term (e.g., “Prescribing”) corresponding to a firstsemantic type (e.g., [ACTION]) indicated by the semantico-syntacticpattern rule 710. The domain-specific lexically-driven pre-parser 110may determine that the input text 888 includes a second term (e.g.,“of”) corresponding to a second syntactic type (e.g., [PREP]) indicatedby the semantico-syntactic pattern rule 710 subsequent to the firstsemantic type (e.g., [ACTION]).

The domain-specific lexically-driven pre-parser 110 may, in response todetermining that the input text 888 matches the pattern indicated by thesemantico-syntactic pattern rule 710, generate the partially parsed andbracketed input text 888 indicating the identified instances ofsyntactic types, semantic types, or a combination thereof. For example,the partially parsed bracketed input text 888 (e.g., “[_(ACTION)Prescribing] [_(PREP) of] [_(DRUG) acetaminophen] [_(PREP) by] [_(AGENT)Dr. Smith] [_(PREP) at][_(LOCATION) Emergency Room]”) may include aseparate phrase marker corresponding to each of the semantic types,syntactic types, or a combination thereof. The domain-specificlexically-driven pre-parser 110 may provide the partially parsed andbracketed input text 882, the partially parsed and bracketed input text884, the partially parsed and bracketed input text 886, the partiallyparsed and bracketed input text 888, or a combination thereof, to thedomain-independent rule-based parser 412.

FIG. 9 illustrates a method 900 for performing domain-specific lexicalanalysis. The method 900 may be performed by the lexical analyzer 108,one or more of the nodes 10 of FIG. 1, the system 300 of FIG. 3, or acombination thereof. In a particular aspect, the domain-specificanalysis 96 may include at least a portion of the method 900.

The method 900 includes performing an analysis of domain-specific corpusto identify a base term and a modifier term, at 902. For example, asdescribed with reference to FIG. 3, the lexical analyzer 108 may performan analysis of the domain-specific corpus 314 to identify the base term322 and the modifier term 334. The modifier term 334 may modify the baseterm 322 in at least a portion of the domain-specific corpus 314.

The method 900 also includes accessing a first entry in lexicon data, at904. For example, as described with reference to FIG. 3, the lexicalanalyzer 108 may access the entry 318 in the lexicon data 316. The entry318 may include the core data 330 corresponding to domain-independentlexical information for the base term 322.

The method 900 further includes adding non-core data to the first entrybased on the analysis, at 906. For example, as described with referenceto FIG. 3, the lexical analyzer 108 may add the non-core data 340 toentry 318 based on the analysis. The non-core data 340 may correspond todomain-specific lexical information for the base term 322. The non-coredata 340 identifies the modifier term 334 as a domain-specific modifierof the base term 322.

The method 900 may thus enable automatic generation of domain-specificinformation corresponding to a base term and updating of the lexicondata 316 to indicate the domain-specific information. In a particularimplementation, the method 900 enables partially automatic generation ofdomain-specific information, update of the lexicon data 316, or both.For example, the lexical analyzer 108 may provide a prompt to a displayindicating the non-core data 340 is going to be added to the entry 318corresponding to the base term 322. The lexical analyzer 108 may add thenon-core data 340 to the entry 318 in response to receiving a user inputconfirming the addition. Automatic (or at least partially automatic)generation of the domain-specific information, update of the lexicondata 316, or both, may conserve resources (e.g., time), reduce (e.g.,eliminate) errors, and improve (e.g., extend) coverage.

FIG. 10 illustrates a method 1000 for performing lexically-drivenparsing. The method 1000 may be performed by the domain-specificlexically-driven pre-parser 110, one or more of the nodes 10 of FIG. 1,the text parser 304, the system 300 of FIG. 3, the domain-independentrule-based parser 412 of FIG. 4, or a combination thereof. In aparticular aspect, the domain-specific analysis 96 may include at leasta portion of the method 1000.

The method 1000 includes obtaining an input text at a text parser, at1002. For example, as described with reference to FIG. 4, the textparser 304 may obtain the input text 414. The text parser 304 mayinclude the domain-specific lexically-driven pre-parser 110 and thedomain-independent rule-based parser 412.

The method 1000 also includes identifying a first term in the inputtext, at 1004. For example, as described with reference to FIG. 4, thedomain-specific lexically-driven pre-parser 110 may identify the term426 in the input text 414.

The method 1000 further includes accessing lexicon data to identify afirst entry corresponding to the first term, at 1006. For example, asdescribed with reference to FIG. 4, the domain-specific lexically-drivenpre-parser 110 may access the lexicon data 316 to identify the entry 318corresponding to the term 426. The entry 318 may include core data 330and the non-core data 340. The core data 330 may correspond todomain-independent lexical information for the term 426. The non-coredata 340 may correspond to domain-specific lexical information for theterm 426.

The method 1000 also includes determining, at the domain-specificlexically-driven pre-parser, that the non-core data of the first entryidentifies a second term in the input text as a modifier of the firstterm, at 1008. For example, as described with reference to FIG. 4, thedomain-specific lexically-driven pre-parser 110 may determine that thenon-core data 340 of the entry 318 identifies the term 424 in the inputtext 414 as a modifier of the term 426.

The method 1000 further includes generating, at the domain-specificlexically-driven pre-parser, a partially parsed and bracketed version ofthe input text, at 1010. For example, as described with reference toFIG. 4, the domain-specific lexically-driven pre-parser 110 may generatethe partially parsed and bracketed input text 480 (e.g., a partiallyparsed and bracketed version of the input text 414). The partiallyparsed and bracketed input text 480 may indicate that the term 424modifies the term 426 in the input text 414.

The method 1000 also includes generating, at the domain-independentrule-based parser, a parsed version of the input text based on thepartially parsed and bracketed version of the input text, at 1012. Forexample, as described with reference to FIG. 4, the domain-independentrule-based parser 412 may generate the parsed text 482 (e.g., a parsedversion of the input text 414) based on the partially parsed andbracketed input text 480.

The method 1000 may thus enable pre-parsing of input text based ondomain-specific information to generate partially parsed and bracketedinput text. Pre-parsing based on the domain-specific information may beperformed prior to parsing based on domain-independent information. Forexample, the partially parsed and bracketed input text may be preparedfor parsing based on domain-independent information. The partiallyparsed and bracketed text may be parsed by a domain-independentrule-based parser. The pre-parsing may reduce (and even, eliminate)syntactic ambiguity in the text, thereby reducing (or eliminating)parsing errors in the parsed text.

FIG. 11 is a block diagram 1100 of a computing environment according toa first aspect that includes electronic components through which thedescribed system may be implemented. The components in FIG. 11 supportaspects of computer-implemented methods and computer-executable programinstructions or code according to the present disclosure. For example,the computing device 1110, or portions thereof, may execute instructionsto perform domain-specific lexical analysis such as described withrespect to the lexical analyzer 108 of FIG. 1, perform domain-specificpre-parsing such as described with respect to the domain-specificlexically-driven pre-parser 110 of FIG. 1, or a combination thereof.

In FIG. 11, the computing device 1110 may include a processor 1112, amain memory 1114, an input/output (I/O) adapter 1146, a non-volatilememory 1118, a memory controller 1120, a bus adapter 1124, a displayadapter 1154, a communications adapter 1150, and a disk drive adapter1142. The I/O adapter 1146 may be configured to interface with one ormore user input devices 1148. For example, the I/O adapter 1146 maycommunicate via serial interfaces (e.g., universal serial bus (USB)interfaces or Institute of Electrical and Electronics Engineers (IEEE)1394 interfaces), parallel interfaces, display adapters, audio adapters,and other interfaces. The user input devices 1148 may include keyboards,pointing devices, displays, speakers, microphones, touch screens,magnetic field generation devices, magnetic field detection devices, andother devices. The processor 1112 may detect interaction events based onuser input received via the I/O adapter 1146. Additionally, theprocessor 1112 may send a graphical user interface (GUI) and relatedelements to a display device via the I/O adapter 1146.

The processor 1112 may include the lexical analyzer 108, thedomain-specific lexically-driven pre-parser 110, or both. The mainmemory 1114 may include volatile memory devices (e.g., random accessmemory (RAM) devices), nonvolatile memory devices (e.g., read-onlymemory (ROM) devices, programmable read-only memory, and flash memory),or both. The main memory 1114 of the computer 1110 includes software,such as an operating system 1132 and software applications 1130. Theoperating system 1132 may include a basic/input output system forbooting the computing device 1110 as well as a full operating system toenable the computing device 1110 to interact with users, other programs,and other devices. The software applications 1130 may include lexicalanalysis application 1133, a domain-specific lexically-drivenpre-parsing application 1135, or both. The lexical analysis application1133 may include, be included within, or correspond to one or more ofthe lexical analyzer 108. The domain-specific lexically-drivenpre-parsing application 1135 may correspond to the domain-specificlexically-driven pre-parser 110. The non-volatile memory 1118 mayinclude a memory 1106. The memory 1106 may correspond to the memory 306of FIG. 3, the memory 406 of FIG. 4, or both.

The display adapter 1154 may be configured to interface with a displaydevice 1156. The communications adapter 1150 may be configured tointerface with the one or more networks 1152. The disk drive adapter1142 may be configured to interface with one or more data storagedevices 1140. The data storage devices 1140 may include nonvolatilestorage devices, such as magnetic disks, optical disks, or flash memorydevices. The data storage devices 1140 may include both removable andnon-removable memory devices. The data storage devices 1140 may beconfigured to store an operating system, images of operating systems,applications, and program data. One or more buses 1144 or othercommunication circuitry may enable the various components of thecomputer 1110 to communicate with one another.

The data storage device 1140, the main memory 1114, the non-volatilememory 1118, the memory 1106, or a combination thereof, may includecomputer-readable storage devices that store instructions executable bythe processor 1112 to cause the processor 1112 to perform certainoperations. For example, the operations may include performing ananalysis of domain-specific corpus to identify a base term and amodifier, accessing an entry in lexicon data, and adding non-core datato the entry identifying the modifier term as a domain-specific modifierof the base term. As another example, the operations may includeobtaining an input text, identifying a first term in the input text,accessing lexicon data to identify an entry corresponding to the firstterm, determining that non-core data of the entry identifies a secondterm of the input text as a modifier of the first term, generating apartially parsed and bracketed version of the input text that indicatesthat the second term modifies the first term, and generating a parsedversion of the input text based on the partially parsed and bracketedversion of the input text.

The present disclosure may include a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some aspects, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to implementations ofthe disclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various aspects of the present disclosure havebeen presented for purposes of illustration, but are not intended to beexhaustive or limited to the aspects disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the described aspects.The terminology used herein was chosen to best explain the principles ofthe aspects, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the aspects disclosed herein.

1. A method comprising: initiating, at an electronic device, an analysisof a domain-specific corpus to identify a base term and a modifier term,wherein the modifier term modifies the base term in at least a portionof the domain-specific corpus; accessing, by the electronic device, afirst entry in lexicon data, wherein the lexicon data is accessible bythe electronic device prior to the initiating and is configured for useat the electronic device in a language processing operation, the firstentry including core data corresponding to domain-independent lexicalinformation for the base term; adding, based on the analysis, non-coredata to the first entry, the non-core data corresponding todomain-specific lexical information for the base term, wherein thenon-core data identifies the modifier term as a domain-specific modifierof the base term; and processing text at the electronic device based onthe non-core data.
 2. The method of claim 1, further comprisingdetermining that the modifier term modifies the base term based on atleast one of co-occurrence statistics or user input.
 3. The method ofclaim 1, wherein: the modifier term includes at least one of anadjectival modifier term, a preposition modifier term, or a nominalmodifier term, and the language processing operation includesprocessing, by a parser, a language sample based on the lexicon data. 4.The method of claim 1, further comprising: identifying, at theelectronic device, the modifier term used with the base term as apreferred domain-specific modifier in the domain-specific corpus; andupdating, at the electronic device, the non-core data to identify themodifier term as a preferred domain-specific modifier of the base term.5. The method of claim 1, further comprising generating, based on thedomain-specific corpus, domain-specific parsing rules for adomain-specific lexically-driven pre-parser.
 6. The method of claim 5,wherein the domain-specific parsing rules include at least one of acollocation rule, a morpho-semantic rule, a named-entity-based patternrule, or a semantico-syntactic pattern rule.
 7. The method of claim 5,further comprising: performing, at the domain-specific lexically-drivenpre-parser, a domain-specific analysis of an input text based on thenon-core data; and providing a partially parsed and bracketed version ofthe input text from the domain-specific lexically-driven pre-parser to adomain-independent rule-based parser.
 8. The method of claim 5, whereinsoftware corresponding to the domain-specific lexically-drivenpre-parser is provided as a service in a cloud computing environment. 9.The method of claim 1, further comprising, based on the analysis of thedomain-specific corpus, updating the non-core data to identify one ormore second modifier terms as one or more additional domain-specificmodifiers of the base term.
 10. The method of claim 1, wherein: prior tothe initiating, the lexicon data is generated by the electronic device,received by the electronic device from another device, provided by auser to the electronic device, or a combination thereof, and thenon-core data includes one or more additional modifier terms for thebase term for a second domain that is distinct from a first domainassociated with the domain-specific corpus.
 11. A method comprising:analyzing, at a device that includes a parser, a domain-specific corpusto identify a base term and a modifier term, wherein the modifier termmodifies the base term in at least a portion of the domain-specificcorpus; accessing a first entry in lexicon data, the lexicon dataaccessible by the device prior to the analyzing, and the first entryincluding core data corresponding to domain-independent lexicalinformation for the base term; adding, based on the analyzing, non-coredata to the first entry, the non-core data corresponding todomain-specific lexical information for the base term, and the non-coredata identifying the modifier term as a domain-specific modifier of thebase term; and performing a language processing operation, at theparser, based on the lexicon data.
 12. The method of claim 11, furthercomprising determining that the modifier term modifies the base termbased on at least one of co-occurrence statistics or user input.
 13. Themethod of claim 11, wherein the modifier term includes at least one ofan adjectival modifier term, a preposition modifier term, or a nominalmodifier term, and wherein the language processing operation includesprocessing a language sample.
 14. The method of claim 11, furthercomprising: identifying, at the device, the modifier term used with thebase term as a preferred domain-specific modifier in the domain-specificcorpus; and updating, at the device, the non-core data to identify themodifier term as a preferred domain-specific modifier of the base term.15. The method of claim 11, further comprising generating, based on thedomain-specific corpus, domain-specific parsing rules for adomain-specific lexically-driven pre-parser.
 16. The method of claim 15,wherein the domain-specific parsing rules include at least one of acollocation rule, a morpho-semantic rule, a named-entity-based patternrule, or a semantico-syntactic pattern rule.
 17. The method of claim 15,wherein the parser includes a domain-independent rule-based parser, andfurther comprising: performing, at the domain-specific lexically-drivenpre-parser, a domain-specific analysis of an input text based on thenon-core data; and providing a partially parsed and bracketed version ofthe input text from the domain-specific lexically-driven pre-parser tothe domain-independent rule-based parser.
 18. The method of claim 15,wherein software corresponding to the domain-specific lexically-drivenpre-parser is provided as a service in a cloud computing environment.19. The method of claim 11, further comprising, based on the analyzing,updating the non-core data to identify one or more second modifier termsas one or more additional domain-specific modifiers of the base term.20. The method of claim 11, wherein: the lexicon data is previouslygenerated by the device, received by the device from another device,provided by a user to the device, or a combination thereof, and thenon-core data includes one or more additional modifier terms for thebase term for a second domain that is distinct from a first domainassociated with the domain-specific corpus.